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2013 IEEE 13th International Conference on Data Mining (2006)
Hong Kong
Dec. 18, 2006 to Dec. 22, 2006
ISSN: 1550-4786
ISBN: 0-7695-2701-9
pp: 803-807
Shenghua Bao , Shanghai Jiao Tong University, China
Bing Liu , University of Illinois at Chicago, USA
Hang Li , Microsoft Research Asia, China
Yunbo Cao , Microsoft Research Asia, China
Yong Yu , Shanghai Jiao Tong University, China
This paper studies the problem of discovering latent associations among objects in text documents. Specifically, given two sets of objects and various types of co-occurrence data concerning the objects existing in texts, we aim to discover the hidden or latent associative relationships between the two sets of objects. Existing methods are not directly applicable as they are unable to consider all this information. For example, the probabilistic mixture model called Separable Mixture Model (SMM) proposed by Hofmann can use only one type of co-occurrences to mine latent associations. This paper proposes a more general probabilistic mixture model called the Typed Separable Mixture Model (TSMM), which is able to use all types of co-occurrences within a single framework. Experimental results based on the expert/expertise mining task show that TSMM outperforms SMM significantly.
Shenghua Bao, Bing Liu, Hang Li, Yunbo Cao, Yong Yu, "Mining Latent Associations of Objects Using a Typed Mixture Model--A Case Study on Expert/Expertise Mining", 2013 IEEE 13th International Conference on Data Mining, vol. 00, no. , pp. 803-807, 2006, doi:10.1109/ICDM.2006.109
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